knitr::opts_chunk$set(echo = TRUE)
rm(list = ls())
#cmdstanr::set_cmdstan_path(path = "C:/Users/kueng/.cmdstan/cmdstan-2.35.0")
#cmdstanr::set_cmdstan_path(path = "C:/Users/pascku/.cmdstan/cmdstan-2.36.0")
library(tidyverse)
library(R.utils)
library(wbCorr)## Warning in update_wbCorr(ask = TRUE): Could not check for updates.
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)
library(see)
library(beepr)
library(DHARMa)
library(digest)
source(file.path('Functions', 'ReportModels.R'))
source(file.path('Functions', 'PrettyTables.R'))
source(file.path('Functions', 'ReportMeasures.R'))
source(file.path('Functions', 'PrepareData.R'))
report_function_hash <- digest::digest(summarize_brms)## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE
do_priorsense = FALSE
get_bayesfactor = TRUE
check_models = TRUE #
if (get_bayesfactor) {
stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'BF', 'Rhat', 'ESS')
} else {
stats_to_report <- c('CI', 'SE', 'pd', 'ROPE', 'Rhat', 'ESS')
}
options(
dplyr.print_max = 100,
brms.backend = 'cmdstan',
brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
brms.file_refit = 'on_change',
#brms.file_refit = 'always',
error = function() {
beepr::beep(sound = 5)
if (shutdown) {
system("shutdown /s /t 180")
quit(save = "no", status = 1)
}
}
, es.use_symbols = TRUE
)
####################### Model parameters #######################
iterations = 12000 # 12'000 per chain to achieve 40'000
warmup = 2000 # 2000
# NO AR!!!
#corstr = 'ar'
#corstr = 'cosy_couple'
#corstr = 'cosy_couple:user'
################################################################
suffix = paste0('_final_with_plan_and_barriers', as.character(iterations))df <- openxlsx::read.xlsx(file.path('long.xlsx'))
df_original <- df
df_double <- prepare_data(df, recode_pushing = TRUE, use_mi = use_mi)[[1]]Constructing scales Re-coding pusing reshaping data (4field) centering data within and between
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s 0.0000 0.0000 0.0000 0.1649 0.0000 5.0000 275
# For indistinguishable Dyads
model_rows_fixed <- c(
'Intercept',
# '-- WITHIN PERSON MAIN EFFECTS --',
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw',
'day',
'plan_selfPlan',
'plan_partnerPlan', # todo: do we need this??
'barriers_self_cw',
'weartime_self_cw',
# '-- BETWEEN PERSON MAIN EFFECTS',
'persuasion_self_cb',
'persuasion_partner_cb',
'pressure_self_cb',
'pressure_partner_cb',
'pushing_self_cb',
'pushing_partner_cb',
'barriers_self_cb',
'weartime_self_cb'
)
model_rows_fixed_ordinal <- c(
model_rows_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rows_fixed[2:length(model_rows_fixed)]
)
model_rows_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
'sd(persuasion_self_cw)',
'sd(persuasion_partner_cw)',
'sd(pressure_self_cw)',
'sd(pressure_partner_cw)',
'sd(pushing_self_cw)',
'sd(pushing_partner_cw)',
# '-- CORRELATION STRUCTURE -- ',
'sigma'
)
model_rows_random_ordinal <- c(model_rows_random,'disc')# For indistinguishable Dyads
model_rownames_fixed <- c(
"Intercept",
# "-- WITHIN PERSON MAIN EFFECTS --",
"Daily individual's experienced persuasion",
"Daily partner's experienced persuasion",
"Daily individual's experienced pressure",
"Daily partner's experienced pressure",
"Daily individual's experienced pushing",
"Daily partner's experienced pushing",
"Day",
"Own actionplan",
'Partner actionplan',
"Daily barriers",
"Daily weartime",
# "-- BETWEEN PERSON MAIN EFFECTS",
"Mean individual's experienced persuasion",
"Mean partner's experienced persuasion",
"Mean individual's experienced pressure",
"Mean partner's experienced pressure",
"Mean individual's experienced pushing",
"Mean partner's experienced pushing",
'Mean barriers',
"Mean weartime"
)
model_rownames_fixed_ordinal <- c(
model_rownames_fixed[1],
'Intercept[1]',
'Intercept[2]',
'Intercept[3]',
'Intercept[4]',
'Intercept[5]',
model_rownames_fixed[2:length(model_rownames_fixed)]
)
model_rownames_random <- c(
# '--------------',
# '-- RANDOM EFFECTS --',
'sd(Intercept)',
"sd(Daily individual's experienced persuasion)",
"sd(Daily partner's experienced persuasion)", # OR partner received
"sd(Daily individual's experienced pressure)",
"sd(Daily partner's experienced pressure)",
"sd(Daily individual's experienced pushing)",
"sd(Daily partner's experienced pushing)",
# '-- CORRELATION STRUCTURE -- ',
'sigma'
)
model_rownames_random_ordinal <- c(model_rownames_random,'disc')rows_to_pack <- list(
"Within-Person Effects" = c(2,12),
"Between-Person Effects" = c(13,20),
"Random Effects" = c(21, 27),
"Additional Parameters" = c(28,28)
)
rows_to_pack_ordinal <- list(
"Within-Person Effects" = c(2+5,12+5),
"Between-Person Effects" = c(13+5,20+5),
"Random Effects" = c(21+5, 27+5),
"Additional Parameters" = c(28+5,28+6)
)## [1] 0 720
formula <- bf(
pa_sub ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
#plan_self + plan_partner +
barriers_self_cw + barriers_self_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | dd | coupleID),
hu = ~ persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
#plan_self + plan_partner +
barriers_self_cw + barriers_self_cb +
day +
# Random effects
(1 + persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | dd | coupleID)
, decomp = 'QR'
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 2)", class = "b", dpar = "hu")
, brms::set_prior("normal(0, 50)", class = "Intercept") # for non-zero PA
, brms::set_prior("normal(0.5, 2.5)", class = "Intercept", dpar = 'hu') # hurdle part
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = hurdle_lognormal()
#)
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_sub <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::hurdle_lognormal(),
#family = brms::hurdle_negbinomial(),
#family = brms::hurdle_poisson(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 42,
file = file.path("models_cache_brms", paste0("pa_sub_hu_lognormal", suffix))
#, file_refit = 'always'
)## Warning: Rows containing NAs were excluded from the model.
if (check_models) {
check_brms(pa_sub, log_pp_check = TRUE)
DHARMa.check_brms.all(pa_sub, integer = TRUE, outliers_type = 'bootstrap')
}## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cw 1.35 [1.30, 1.40] 1.16 0.74
## persuasion_partner_cw 1.07 [1.05, 1.12] 1.04 0.93
## pressure_self_cw 1.05 [1.02, 1.09] 1.02 0.96
## pressure_partner_cw 1.06 [1.03, 1.10] 1.03 0.95
## pushing_self_cw 1.03 [1.01, 1.08] 1.02 0.97
## pushing_partner_cw 1.13 [1.10, 1.17] 1.06 0.88
## persuasion_self_cb 1.04 [1.02, 1.09] 1.02 0.96
## persuasion_partner_cb 3.65 [3.48, 3.84] 1.91 0.27
## pressure_self_cb 3.59 [3.42, 3.78] 1.90 0.28
## pressure_partner_cb 2.33 [2.23, 2.45] 1.53 0.43
## pushing_self_cb 2.31 [2.21, 2.42] 1.52 0.43
## pushing_partner_cb 3.23 [3.08, 3.40] 1.80 0.31
## barriers_self_cw 3.28 [3.12, 3.45] 1.81 0.30
## barriers_self_cb 1.01 [1.00, 1.37] 1.00 0.99
## day 1.14 [1.10, 1.18] 1.07 0.88
## Tolerance 95% CI
## [0.71, 0.77]
## [0.90, 0.95]
## [0.92, 0.98]
## [0.91, 0.97]
## [0.92, 0.99]
## [0.85, 0.91]
## [0.92, 0.98]
## [0.26, 0.29]
## [0.26, 0.29]
## [0.41, 0.45]
## [0.41, 0.45]
## [0.29, 0.32]
## [0.29, 0.32]
## [0.73, 1.00]
## [0.85, 0.91]
## # Distribution of Model Family
##
## Predicted Distribution of Residuals
##
## Distribution Probability
## cauchy 91%
## lognormal 9%
##
## Predicted Distribution of Response
##
## Distribution Probability
## neg. binomial (zero-infl.) 84%
## beta-binomial 9%
## lognormal 6%
##
## Divergences:
## 16 of 40000 iterations ended with a divergence (0.04%).
## Try increasing 'adapt_delta' to remove the divergences.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 6 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 4, observations = 1776, p-value = 0.64
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.000000000 0.003941441
## sample estimates:
## outlier frequency (expected: 0.00160472972972973 )
## 0.002252252
if (do_priorsense) {
priorsense_vars <- c(
'Intercept',
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
)
hurdle_priorsense_vars <- c(
'Intercept_hu',
'b_hu_persuasion_self_cw',
'b_hu_persuasion_partner_cw',
'b_hu_pressure_self_cw',
'b_hu_pressure_partner_cw',
'b_hu_pushing_self_cw',
'b_hu_pushing_partner_cw'
)
gc()
priorsense::powerscale_sensitivity(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
priorsense::powerscale_plot_dens(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
priorsense::powerscale_plot_ecdf(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
priorsense::powerscale_plot_quantities(pa_sub, variable = c(priorsense_vars, hurdle_priorsense_vars))
}# rope range for continuous part of the model
rope_factor <- sd(log(pa_sub$data$pa_sub[pa_sub$data$pa_sub > 0]))
rope_range_continuous = c(-0.1 * rope_factor, 0.1 * rope_factor)
summary_pa_sub <- summarize_brms(
pa_sub,
stats_to_report = stats_to_report,
rope_range = rope_range_continuous,
hu_rope_range = c(-0.18, 0.18),
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) ## Warning: There were 16 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning in summarize_brms(pa_sub, stats_to_report = stats_to_report, rope_range
## = rope_range_continuous, : Coefficients were exponentiated. Double check if
## this was intended.
| exp(Est.)_hu | SE_hu | 95% CI_hu | pd_hu | ROPE_hu | inside ROPE_hu | Rhat_hu | Bulk_ESS_hu | Tail_ESS_hu | exp(Est.)_nonzero | SE_nonzero | 95% CI_nonzero | pd_nonzero | ROPE_nonzero | inside ROPE_nonzero | Rhat_nonzero | Bulk_ESS_nonzero | Tail_ESS_nonzero | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 2.49*** | 0.60 | [ 1.52, 4.04] | 1.000 | [0.84, 1.20] | 0.002 | 1.000 | 28779 | 9420 | 49.30*** | 4.00 | [41.99, 57.88] | 1.000 | [0.93, 1.08] | 0.000 | 1.000 | 12806 | 4464 |
| Within-Person Effects | ||||||||||||||||||
| Daily individual’s experienced persuasion | 1.47*** | 0.14 | [ 1.23, 1.80] | 1.000 | [0.84, 1.20] | 0.011 | 1.000 | 53281 | 29133 | 1.04 | 0.03 | [ 0.99, 1.10] | 0.928 | [0.93, 1.08] | 0.892 | 1.000 | 28911 | 31485 |
| Daily partner’s experienced persuasion | 1.32** | 0.13 | [ 1.10, 1.63] | 0.999 | [0.84, 1.20] | 0.151 | 1.000 | 46599 | 28680 | 1.02 | 0.02 | [ 0.98, 1.07] | 0.833 | [0.93, 1.08] | 0.983 | 1.000 | 41288 | 31157 |
| Daily individual’s experienced pressure | 0.81 | 0.20 | [ 0.47, 1.48] | 0.803 | [0.84, 1.20] | 0.366 | 1.000 | 37584 | 24938 | 0.89 | 0.05 | [ 0.78, 1.01] | 0.970 | [0.93, 1.08] | 0.234 | 1.000 | 44773 | 32842 |
| Daily partner’s experienced pressure | 1.48 | 0.57 | [ 0.81, 4.99] | 0.894 | [0.84, 1.20] | 0.233 | 1.001 | 8333 | 4109 | 0.95 | 0.05 | [ 0.86, 1.05] | 0.846 | [0.93, 1.08] | 0.708 | 1.000 | 45145 | 36498 |
| Daily individual’s experienced pushing | 1.16 | 0.16 | [ 0.87, 1.57] | 0.853 | [0.84, 1.20] | 0.578 | 1.000 | 42674 | 31212 | 0.99 | 0.03 | [ 0.93, 1.05] | 0.672 | [0.93, 1.08] | 0.972 | 1.000 | 40934 | 37135 |
| Daily partner’s experienced pushing | 1.58** | 0.27 | [ 1.17, 2.44] | 0.998 | [0.84, 1.20] | 0.035 | 1.000 | 35300 | 25146 | 0.98 | 0.03 | [ 0.92, 1.04] | 0.733 | [0.93, 1.08] | 0.965 | 1.000 | 44587 | 33965 |
| Day | 0.97 | 0.21 | [ 0.64, 1.49] | 0.558 | [0.84, 1.20] | 0.593 | 1.000 | 85112 | 30070 | 1.01 | 0.07 | [ 0.88, 1.16] | 0.565 | [0.93, 1.08] | 0.726 | 1.000 | 81511 | 31065 |
| Own actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Partner actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Daily barriers | 1.21*** | 0.06 | [ 1.10, 1.33] | 1.000 | [0.84, 1.20] | 0.402 | 1.000 | 85512 | 27584 | 1.04** | 0.01 | [ 1.01, 1.07] | 0.998 | [0.93, 1.08] | 0.992 | 1.000 | 78928 | 29748 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | ||||||||||||||||||
| Mean individual’s experienced persuasion | 0.87 | 0.47 | [ 0.30, 2.52] | 0.599 | [0.84, 1.20] | 0.255 | 1.000 | 24823 | 29147 | 1.06 | 0.17 | [ 0.77, 1.47] | 0.654 | [0.93, 1.08] | 0.339 | 1.000 | 25770 | 30233 |
| Mean partner’s experienced persuasion | 1.26 | 0.67 | [ 0.44, 3.57] | 0.668 | [0.84, 1.20] | 0.244 | 1.000 | 21974 | 24558 | 0.98 | 0.15 | [ 0.71, 1.34] | 0.561 | [0.93, 1.08] | 0.368 | 1.000 | 26298 | 30138 |
| Mean individual’s experienced pressure | 0.30 | 0.22 | [ 0.06, 1.18] | 0.957 | [0.84, 1.20] | 0.049 | 1.000 | 31575 | 31174 | 0.66 | 0.26 | [ 0.29, 1.45] | 0.855 | [0.93, 1.08] | 0.086 | 1.000 | 10629 | 18219 |
| Mean partner’s experienced pressure | 0.25* | 0.18 | [ 0.05, 1.00] | 0.975 | [0.84, 1.20] | 0.034 | 1.000 | 36444 | 32635 | 0.52 | 0.21 | [ 0.23, 1.14] | 0.948 | [0.93, 1.08] | 0.041 | 1.001 | 9513 | 9057 |
| Mean individual’s experienced pushing | 1.15 | 0.92 | [ 0.24, 5.47] | 0.566 | [0.84, 1.20] | 0.172 | 1.000 | 32787 | 31999 | 1.54 | 0.45 | [ 0.86, 2.75] | 0.932 | [0.93, 1.08] | 0.069 | 1.000 | 14811 | 23441 |
| Mean partner’s experienced pushing | 1.47 | 1.20 | [ 0.31, 7.49] | 0.688 | [0.84, 1.20] | 0.158 | 1.000 | 32576 | 32337 | 1.57 | 0.46 | [ 0.88, 2.82] | 0.938 | [0.93, 1.08] | 0.064 | 1.000 | 14270 | 22897 |
| Mean barriers | 1.36** | 0.16 | [ 1.07, 1.72] | 0.995 | [0.84, 1.20] | 0.149 | 1.000 | 40975 | 17387 | 1.08* | 0.04 | [ 1.01, 1.16] | 0.989 | [0.93, 1.08] | 0.461 | 1.000 | 54078 | 31977 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | ||||||||||||||||||
| sd(Intercept) | 1.21 | 0.18 | [0.91, 1.65] | NA | NA | NA | 1.000 | 23987 | 28447 | 0.30 | 0.04 | [0.22, 0.40] | NA | NA | NA | 1.000 | 18577 | 26589 |
| sd(Daily individual’s experienced persuasion) | 0.17 | 0.13 | [0.01, 0.44] | NA | NA | NA | 1.000 | 18163 | 20752 | 0.11 | 0.03 | [0.06, 0.17] | NA | NA | NA | 1.000 | 21196 | 23657 |
| sd(Daily partner’s experienced persuasion) | 0.22 | 0.14 | [0.01, 0.50] | NA | NA | NA | 1.000 | 15918 | 17927 | 0.06 | 0.03 | [0.01, 0.12] | NA | NA | NA | 1.000 | 16213 | 13982 |
| sd(Daily individual’s experienced pressure) | 0.43 | 0.40 | [0.02, 1.83] | NA | NA | NA | 1.000 | 15737 | 24567 | 0.07 | 0.06 | [0.00, 0.26] | NA | NA | NA | 1.000 | 22063 | 22306 |
| sd(Daily partner’s experienced pressure) | 0.77 | 0.60 | [0.04, 2.39] | NA | NA | NA | 1.001 | 7796 | 4340 | 0.06 | 0.05 | [0.00, 0.19] | NA | NA | NA | 1.000 | 26330 | 25109 |
| sd(Daily individual’s experienced pushing) | 0.43 | 0.23 | [0.04, 0.94] | NA | NA | NA | 1.000 | 12790 | 15234 | 0.05 | 0.04 | [0.00, 0.14] | NA | NA | NA | 1.000 | 16940 | 20146 |
| sd(Daily partner’s experienced pushing) | 0.42 | 0.29 | [0.03, 1.15] | NA | NA | NA | 1.000 | 12261 | 19089 | 0.05 | 0.04 | [0.00, 0.14] | NA | NA | NA | 1.000 | 17453 | 20691 |
| Additional Parameters | ||||||||||||||||||
| sigma | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0.67 | 0.01 | [0.65, 0.70] | NA | NA | NA | 1.000 | 65607 | 30236 |
# Plot continuous part of model
variable <- c(
'(Intercept)',
'b_persuasion_self_cw',
'b_persuasion_partner_cw',
'b_pressure_self_cw',
'b_pressure_partner_cw',
'b_pushing_self_cw',
'b_pushing_partner_cw'
)
plot(
bayestestR::p_direction(pa_sub, parameter = variable),
priors = TRUE
) + theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
plot(
bayestestR::rope(
pa_sub,
parameter = variable,
range = rope_range_continuous,
verbose = F,
ci = 1
)
) + theme_bw()# Hurdle part of the model
variable <- c(
'b_hu_persuasion_self_cw',
'b_hu_persuasion_partner_cw',
'b_hu_pressure_self_cw',
'b_hu_pressure_partner_cw',
'b_hu_pushing_self_cw',
'b_hu_pushing_partner_cw'
)
plot(
bayestestR::p_direction(pa_sub, parameter = variable),
priors = TRUE
) + theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
# The rope range for the bernoulli part of the model is -0.18, 0.18
plot(
bayestestR::rope(pa_sub, parameter = variable, range = c(-0.18, 0.18), ci = 1),
verbose = FALSE
) + theme_bw()## Possible multicollinearity between b_hu_persuasion_partner_cb and
## b_hu_persuasion_self_cb (r = 0.75), b_persuasion_partner_cb and
## b_persuasion_self_cb (r = 0.7), b_pressure_partner_cb and
## b_pressure_self_cb (r = 0.8). This might lead to inappropriate results.
## See 'Details' in '?rope'.
Hurdle part of the model on the left, non-zero part towards the right side of the table
conds_eff <- conditional_spaghetti(
pa_sub,
effects = c(
'persuasion_self_cw',
'persuasion_partner_cw',
'pressure_self_cw',
'pressure_partner_cw',
'pushing_self_cw',
'pushing_partner_cw'
),
x_label = c(
'Received Persuasion',
'Exerted Persuasion',
'Received Pressure',
'Exerted Pressure',
'Received Plan-Related Pushing',
'Exerted Plan-Related Pushing'
),
group_var = 'coupleID',
plot_full_range = TRUE,
y_limits = c(0, 100),
y_label = "Same-Day MVPA",
y_labels = c('Probability of Being Active', 'Minutes of MVPA When Active', 'Overall Expected Minutes of MVPA'),
, filter_quantiles = .9995
, font_family = 'Candara'
)## This is posterior version 1.6.0
##
## Attaching package: 'posterior'
## The following object is masked from 'package:bayesplot':
##
## rhat
## The following objects are masked from 'package:stats':
##
## mad, sd, var
## The following objects are masked from 'package:base':
##
## %in%, match
## Registering fonts with R
## Scanning ttf files in C:\Windows/Fonts, C:\Users\pascku\AppData\Local/Microsoft/Windows/Fonts ...
## Extracting .afm files from .ttf files...
## C:\Windows\Fonts\Candara.ttf : Candara already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candarab.ttf : Candara-Bold already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candarai.ttf : Candara-Italic already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candaral.ttf : Candara-Light already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candarali.ttf : Candara-LightItalic already registered in fonts database. Skipping.
## C:\Windows\Fonts\Candaraz.ttf : Candara-BoldItalic already registered in fonts database. Skipping.
## Found FontName for 0 fonts.
## Scanning afm files in C:/Users/pascku/AppData/Local/R/cache/R/renv/cache/v5/windows/R-4.4/x86_64-w64-mingw32/extrafontdb/1.0/a861555ddec7451c653b40e713166c6f/extrafontdb/metrics
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
## Warning: Dropping 'draws_df' class as required metadata was removed.
$persuasion_self_cw
## Warning: Removed 142 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 230 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.00774
$persuasion_partner_cw
## Warning: Removed 95 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 201 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0069
$pressure_self_cw
## Warning: Removed 52 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Warning: Removed 91 rows containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0124
$pressure_partner_cw
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
## Picking joint bandwidth of 0.0277
$pushing_self_cw
## Picking joint bandwidth of 0.00893
$pushing_partner_cw
## Picking joint bandwidth of 0.0129
Note. This graphic illustrates the relationship between
social control and moderate to vigorous physical activity (MVPA) using a
Bayesian Hurdle-Lognormal Multilevel Model. The predictor is centered
within individuals to examine how deviations from their average social
control relate to same-day MVPA. Shaded areas indicate credible
intervals, thick lines show fixed effects, and thin lines represent
random effects, highlighting variability across couples. The plots
display the probability of being active, expected minutes of MVPA when
active, and combined predicted MVPA. The bottom density plot visualizes
the posterior distributions of slope estimates, transformed to represent
multiplicative changes in odds ratios (hurdle component) or expected
values. Medians and 95% credible intervals (2.5th and 97.5th
percentiles) are shown. Effects are significant, when the 95% credible
interval does not overlap 1.
x_label = c(
'Received Persuasion',
'Exerted Persuasion',
'Received Pressure',
'Exerted Pressure',
'Received Plan-Related Pushing',
'Exerted Plan-Related Pushing'
)
home_dir <- getwd()
output_dir <- file.path(home_dir, 'Output', 'Plots')
for (i in 1:length(conds_eff)) {
effname <- names(conds_eff)[i]
eff_plot <- conds_eff[[i]]
x_label_i <- x_label[[i]]
rmarkdown::render(
file.path(output_dir, 'BeautifulPlotWithNote.Rmd'),
output_file = file.path(output_dir, paste0('Graphic_', effname, '.pdf')),
params = list(
home_dir = home_dir,
output_dir = output_dir,
p_i = eff_plot,
p_name = effname,
x_label = x_label_i
),
envir = new.env(),
quiet = TRUE
)
}
print('done')## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (pressure_self_cw... < 0 -0.11 0.08 -0.23 0.02 11.23
## Post.Prob Star
## 1 0.92
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
## [1] 5.75 971.25
formula <- bf(
pa_obj ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
#plan_self + plan_partner +
barriers_self_cw + barriers_self_cb +
day + weartime_self_cw + weartime_self_cb +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
, decomp = 'QR'
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 50)", class = "Intercept")
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = lognormal()
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
pa_obj_log <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = lognormal(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("pa_obj_log_gaussian", suffix))
)## Warning: Rows containing NAs were excluded from the model.
if (check_models) {
check_brms(pa_obj_log, log_pp_check = TRUE)
DHARMa.check_brms.all(pa_obj_log, integer = TRUE, outliers_type = 'bootstrap')
}## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cw 1.11 [1.07, 1.17] 1.05 0.90
## persuasion_partner_cw 1.15 [1.11, 1.21] 1.07 0.87
## pressure_self_cw 1.08 [1.05, 1.14] 1.04 0.93
## pressure_partner_cw 1.10 [1.07, 1.16] 1.05 0.91
## pushing_self_cw 1.17 [1.13, 1.23] 1.08 0.85
## pushing_partner_cw 1.16 [1.12, 1.22] 1.08 0.86
## pressure_self_cb 3.31 [3.10, 3.55] 1.82 0.30
## pressure_partner_cb 3.51 [3.28, 3.76] 1.87 0.29
## barriers_self_cw 1.01 [1.00, 1.37] 1.01 0.99
## barriers_self_cb 1.07 [1.04, 1.14] 1.04 0.93
## day 1.06 [1.03, 1.13] 1.03 0.94
## weartime_self_cw 1.06 [1.03, 1.12] 1.03 0.95
## weartime_self_cb 1.21 [1.16, 1.27] 1.10 0.83
## Tolerance 95% CI
## [0.85, 0.93]
## [0.83, 0.90]
## [0.88, 0.96]
## [0.86, 0.94]
## [0.81, 0.89]
## [0.82, 0.90]
## [0.28, 0.32]
## [0.27, 0.30]
## [0.73, 1.00]
## [0.88, 0.96]
## [0.89, 0.97]
## [0.89, 0.97]
## [0.79, 0.86]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cb 5.97 [5.56, 6.42] 2.44 0.17
## persuasion_partner_cb 6.44 [5.99, 6.92] 2.54 0.16
## pushing_self_cb 5.19 [4.84, 5.58] 2.28 0.19
## pushing_partner_cb 5.13 [4.78, 5.51] 2.26 0.20
## Tolerance 95% CI
## [0.16, 0.18]
## [0.14, 0.17]
## [0.18, 0.21]
## [0.18, 0.21]
## # Distribution of Model Family
##
## Predicted Distribution of Residuals
##
## Distribution Probability
## cauchy 88%
## lognormal 9%
## weibull 3%
##
## Predicted Distribution of Response
##
## Distribution Probability
## lognormal 72%
## tweedie 16%
## neg. binomial (zero-infl.) 9%
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 2 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
## DHARMa:testOutliers with type = binomial may have inflated Type I error rates for integer-valued distributions. To get a more exact result, it is recommended to re-run testOutliers with type = 'bootstrap'. See ?testOutliers for details
##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 14, observations = 1594, p-value < 2.2e-16
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.0006273526 0.0059755332
## sample estimates:
## outlier frequency (expected: 0.00308030112923463 )
## 0.008782936
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(pa_obj_log, variable = priorsense_vars)
priorsense::powerscale_plot_dens(pa_obj_log, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(pa_obj_log, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(pa_obj_log, variable = priorsense_vars)
}# rope range for lognormal model
rope_factor <- sd(log(pa_obj_log$data$pa_obj))
rope_range_log = c(-0.1 * rope_factor, 0.1 * rope_factor)
summary_pa_obj <- summarize_brms(
pa_obj_log,
stats_to_report = stats_to_report,
rope_range = rope_range_log,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T) ## Warning in summarize_brms(pa_obj_log, stats_to_report = stats_to_report, :
## Coefficients were exponentiated. Double check if this was intended.
| exp(Est.) | SE | 95% CI | pd | ROPE | inside ROPE | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|
| Intercept | 123.18*** | 7.40 | [109.03, 139.10] | 1.000 | [0.94, 1.06] | 0.000 | 1.000 | 10287 | 18834 |
| Within-Person Effects | |||||||||
| Daily individual’s experienced persuasion | 1.02 | 0.02 | [ 0.99, 1.06] | 0.881 | [0.94, 1.06] | 0.987 | 1.000 | 32647 | 32365 |
| Daily partner’s experienced persuasion | 1.03 | 0.02 | [ 0.99, 1.07] | 0.940 | [0.94, 1.06] | 0.970 | 1.000 | 43952 | 31850 |
| Daily individual’s experienced pressure | 0.95 | 0.04 | [ 0.86, 1.04] | 0.870 | [0.94, 1.06] | 0.556 | 1.000 | 43518 | 30052 |
| Daily partner’s experienced pressure | 0.97 | 0.04 | [ 0.90, 1.04] | 0.814 | [0.94, 1.06] | 0.763 | 1.000 | 56254 | 32810 |
| Daily individual’s experienced pushing | 1.03 | 0.03 | [ 0.97, 1.08] | 0.818 | [0.94, 1.06] | 0.904 | 1.000 | 42843 | 33705 |
| Daily partner’s experienced pushing | 1.01 | 0.02 | [ 0.96, 1.06] | 0.629 | [0.94, 1.06] | 0.982 | 1.000 | 50585 | 31498 |
| Day | 0.97 | 0.05 | [ 0.88, 1.07] | 0.746 | [0.94, 1.06] | 0.693 | 1.000 | 76155 | 29860 |
| Own actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Partner actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Daily barriers | 1.03** | 0.01 | [ 1.01, 1.05] | 0.999 | [0.94, 1.06] | 0.999 | 1.000 | 77752 | 27470 |
| Daily weartime | 1.00*** | 0.00 | [ 1.00, 1.00] | 1.000 | [0.94, 1.06] | 1.000 | 1.000 | 82611 | 30094 |
| Between-Person Effects | |||||||||
| Mean individual’s experienced persuasion | 1.02 | 0.16 | [ 0.74, 1.39] | 0.547 | [0.94, 1.06] | 0.310 | 1.000 | 9251 | 17057 |
| Mean partner’s experienced persuasion | 0.87 | 0.14 | [ 0.63, 1.19] | 0.814 | [0.94, 1.06] | 0.210 | 1.000 | 9170 | 16781 |
| Mean individual’s experienced pressure | 1.05 | 0.19 | [ 0.74, 1.49] | 0.603 | [0.94, 1.06] | 0.261 | 1.000 | 14364 | 23206 |
| Mean partner’s experienced pressure | 1.04 | 0.18 | [ 0.74, 1.47] | 0.595 | [0.94, 1.06] | 0.271 | 1.000 | 12665 | 21665 |
| Mean individual’s experienced pushing | 1.12 | 0.26 | [ 0.71, 1.78] | 0.696 | [0.94, 1.06] | 0.190 | 1.000 | 12416 | 20531 |
| Mean partner’s experienced pushing | 1.29 | 0.29 | [ 0.83, 2.05] | 0.874 | [0.94, 1.06] | 0.112 | 1.000 | 12435 | 20072 |
| Mean barriers | 1.05 | 0.03 | [ 0.99, 1.10] | 0.958 | [0.94, 1.06] | 0.744 | 1.000 | 45006 | 32464 |
| Mean weartime | 1.00* | 0.00 | [ 1.00, 1.00] | 0.983 | [0.94, 1.06] | 1.000 | 1.000 | 37030 | 33323 |
| Random Effects | |||||||||
| sd(Intercept) | 0.32 | 0.04 | [0.25, 0.43] | NA | NA | NA | 1.000 | 12078 | 20334 |
| sd(Daily individual’s experienced persuasion) | 0.05 | 0.02 | [0.02, 0.09] | NA | NA | NA | 1.000 | 19548 | 14612 |
| sd(Daily partner’s experienced persuasion) | 0.05 | 0.02 | [0.00, 0.10] | NA | NA | NA | 1.000 | 11945 | 12399 |
| sd(Daily individual’s experienced pressure) | 0.06 | 0.06 | [0.00, 0.23] | NA | NA | NA | 1.000 | 16266 | 23012 |
| sd(Daily partner’s experienced pressure) | 0.04 | 0.03 | [0.00, 0.13] | NA | NA | NA | 1.000 | 25114 | 22322 |
| sd(Daily individual’s experienced pushing) | 0.09 | 0.03 | [0.02, 0.16] | NA | NA | NA | 1.000 | 12694 | 10874 |
| sd(Daily partner’s experienced pushing) | 0.05 | 0.03 | [0.00, 0.12] | NA | NA | NA | 1.000 | 14159 | 17910 |
| Additional Parameters | |||||||||
| sigma | 0.54 | 0.01 | [0.52, 0.56] | NA | NA | NA | 1.000 | 57239 | 28922 |
plot(
bayestestR::p_direction(pa_obj_log),
priors = TRUE
) +
coord_cartesian(xlim = c(-3, 3)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
## Possible multicollinearity between b_persuasion_partner_cb and
## b_persuasion_self_cb (r = 0.82), b_pushing_partner_cb and
## b_pushing_self_cb (r = 0.75). This might lead to inappropriate results.
## See 'Details' in '?rope'.
## [1] 0 5
formula <- bf(
aff ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
#plan_self + plan_partner +
barriers_self_cw + barriers_self_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
, decomp = 'QR'
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 5)", class = "b")
,brms::set_prior("normal(0, 20)", class = "Intercept", lb=1, ub=6)
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
, brms::set_prior("student_t(3, 0, 2.5)", class = "sigma", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = gaussian()
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
mood_gauss <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = gaussian(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("mood_gauss", suffix))
)## Warning: Rows containing NAs were excluded from the model.
if (check_models) {
check_brms(mood_gauss, log_pp_check = FALSE)
DHARMa.check_brms.all(mood_gauss, integer = FALSE)
}## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cw 1.17 [1.13, 1.23] 1.08 0.85
## persuasion_partner_cw 1.14 [1.10, 1.20] 1.07 0.88
## pressure_self_cw 1.13 [1.09, 1.19] 1.06 0.88
## pressure_partner_cw 1.07 [1.04, 1.13] 1.04 0.93
## pushing_self_cw 1.26 [1.21, 1.33] 1.12 0.79
## pushing_partner_cw 1.13 [1.09, 1.19] 1.07 0.88
## pressure_self_cb 4.88 [4.55, 5.23] 2.21 0.21
## pressure_partner_cb 4.77 [4.46, 5.11] 2.18 0.21
## barriers_self_cw 1.01 [1.00, 1.84] 1.00 0.99
## barriers_self_cb 1.07 [1.04, 1.13] 1.04 0.93
## day 1.07 [1.04, 1.13] 1.03 0.93
## Tolerance 95% CI
## [0.81, 0.89]
## [0.84, 0.91]
## [0.84, 0.92]
## [0.88, 0.96]
## [0.75, 0.83]
## [0.84, 0.91]
## [0.19, 0.22]
## [0.20, 0.22]
## [0.54, 1.00]
## [0.88, 0.96]
## [0.88, 0.96]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cb 9.80 [9.12, 10.55] 3.13 0.10
## persuasion_partner_cb 9.64 [8.97, 10.37] 3.11 0.10
## pushing_self_cb 7.06 [6.58, 7.58] 2.66 0.14
## pushing_partner_cb 6.98 [6.51, 7.50] 2.64 0.14
## Tolerance 95% CI
## [0.09, 0.11]
## [0.10, 0.11]
## [0.13, 0.15]
## [0.13, 0.15]
## # Distribution of Model Family
##
## Predicted Distribution of Residuals
##
## Distribution Probability
## cauchy 44%
## normal 41%
## exponential 6%
##
## Predicted Distribution of Response
##
## Distribution Probability
## tweedie 41%
## beta-binomial 38%
## half-cauchy 6%
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: model.check
## outliers at both margin(s) = 14, observations = 1776, p-value =
## 2.096e-05
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
## 0.004316152 0.013190801
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 )
## 0.007882883
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(mood_gauss, variable = priorsense_vars)
priorsense::powerscale_plot_dens(mood_gauss, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(mood_gauss, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(mood_gauss, variable = priorsense_vars)
}summary_mood <- summarize_brms(
mood_gauss,
stats_to_report = stats_to_report,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = F)
summary_mood %>%
print_df(rows_to_pack = rows_to_pack)| Est. | SE | 95% CI | pd | ROPE | inside ROPE | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|
| Intercept | 3.80*** | 0.11 | [ 3.58, 4.02] | 1.000 | [-0.11, 0.11] | 0.000 | 1.001 | 8225 | 15991 |
| Within-Person Effects | |||||||||
| Daily individual’s experienced persuasion | 0.01 | 0.02 | [-0.04, 0.06] | 0.620 | [-0.11, 0.11] | 1.000 | 1.000 | 51887 | 32038 |
| Daily partner’s experienced persuasion | 0.01 | 0.02 | [-0.04, 0.06] | 0.678 | [-0.11, 0.11] | 1.000 | 1.000 | 59637 | 31864 |
| Daily individual’s experienced pressure | -0.05 | 0.06 | [-0.18, 0.08] | 0.763 | [-0.11, 0.11] | 0.833 | 1.000 | 45767 | 28568 |
| Daily partner’s experienced pressure | -0.06 | 0.06 | [-0.19, 0.06] | 0.844 | [-0.11, 0.11] | 0.791 | 1.000 | 44947 | 28359 |
| Daily individual’s experienced pushing | 0.04 | 0.04 | [-0.04, 0.11] | 0.837 | [-0.11, 0.11] | 0.975 | 1.000 | 49871 | 34302 |
| Daily partner’s experienced pushing | 0.11* | 0.04 | [ 0.02, 0.18] | 0.993 | [-0.11, 0.11] | 0.542 | 1.000 | 41442 | 29456 |
| Day | 0.24** | 0.08 | [ 0.09, 0.39] | 0.999 | [-0.11, 0.11] | 0.044 | 1.000 | 72175 | 29192 |
| Own actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Partner actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Daily barriers | 0.13*** | 0.02 | [ 0.10, 0.16] | 1.000 | [-0.11, 0.11] | 0.137 | 1.000 | 78022 | 28614 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | |||||||||
| Mean individual’s experienced persuasion | 0.14 | 0.29 | [-0.44, 0.72] | 0.689 | [-0.11, 0.11] | 0.266 | 1.000 | 7958 | 14644 |
| Mean partner’s experienced persuasion | 0.43 | 0.29 | [-0.15, 1.00] | 0.928 | [-0.11, 0.11] | 0.103 | 1.000 | 7666 | 14449 |
| Mean individual’s experienced pressure | -0.18 | 0.30 | [-0.78, 0.41] | 0.728 | [-0.11, 0.11] | 0.242 | 1.000 | 10073 | 19492 |
| Mean partner’s experienced pressure | -0.31 | 0.30 | [-0.90, 0.28] | 0.852 | [-0.11, 0.11] | 0.172 | 1.000 | 9729 | 18338 |
| Mean individual’s experienced pushing | 0.28 | 0.41 | [-0.54, 1.12] | 0.753 | [-0.11, 0.11] | 0.170 | 1.000 | 10182 | 17943 |
| Mean partner’s experienced pushing | -0.07 | 0.41 | [-0.89, 0.77] | 0.564 | [-0.11, 0.11] | 0.208 | 1.000 | 10015 | 17318 |
| Mean barriers | 0.36*** | 0.04 | [ 0.28, 0.44] | 1.000 | [-0.11, 0.11] | 0.000 | 1.000 | 49900 | 32196 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||
| sd(Intercept) | 0.61 | 0.08 | [0.48, 0.80] | NA | NA | NA | 1.000 | 11221 | 20168 |
| sd(Daily individual’s experienced persuasion) | 0.04 | 0.03 | [0.00, 0.12] | NA | NA | NA | 1.000 | 13475 | 17798 |
| sd(Daily partner’s experienced persuasion) | 0.04 | 0.03 | [0.00, 0.11] | NA | NA | NA | 1.000 | 14426 | 19079 |
| sd(Daily individual’s experienced pressure) | 0.07 | 0.06 | [0.00, 0.27] | NA | NA | NA | 1.000 | 21788 | 21202 |
| sd(Daily partner’s experienced pressure) | 0.09 | 0.07 | [0.00, 0.28] | NA | NA | NA | 1.000 | 18175 | 20881 |
| sd(Daily individual’s experienced pushing) | 0.07 | 0.05 | [0.00, 0.18] | NA | NA | NA | 1.000 | 13031 | 16597 |
| sd(Daily partner’s experienced pushing) | 0.10 | 0.05 | [0.01, 0.21] | NA | NA | NA | 1.000 | 13954 | 13987 |
| Additional Parameters | |||||||||
| sigma | 0.88 | 0.02 | [0.85, 0.91] | NA | NA | NA | 1.000 | 62268 | 29718 |
plot(
bayestestR::p_direction(mood_gauss),
priors = TRUE
) +
coord_cartesian(xlim = c(-3, 3)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
## Possible multicollinearity between b_persuasion_partner_cb and
## b_persuasion_self_cb (r = 0.9), b_pressure_self_cb and
## b_persuasion_self_cb (r = 0.76), b_pressure_partner_cb and
## b_persuasion_self_cb (r = 0.73), b_pressure_self_cb and
## b_persuasion_partner_cb (r = 0.73), b_pressure_partner_cb and
## b_persuasion_partner_cb (r = 0.75), b_pushing_partner_cb and
## b_pushing_self_cb (r = 0.83). This might lead to inappropriate results.
## See 'Details' in '?rope'.
conditional_spaghetti(
mood_gauss,
effects = c('pushing_partner_cw'),
group_var = 'coupleID',
plot_full_range = TRUE
)$pushing_partner_cw
## [1] 0 5
df_double$reactance_ordinal <- factor(df_double$reactance,
levels = 0:5,
ordered = TRUE)
formula <- bf(
reactance_ordinal ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
#plan_self + plan_partner +
barriers_self_cw + barriers_self_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
, decomp = 'QR'
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = cumulative() # HURDLE_CUMULATIVE
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
reactance_ordinal <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::cumulative(),
#control = list(adapt_delta = 0.95),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777
, file = file.path("models_cache_brms", paste0("reactance_ordinal", suffix))
)## Warning: Rows containing NAs were excluded from the model.
if (check_models) {
check_brms(reactance_ordinal)
DHARMa.check_brms.all(reactance_ordinal, outliers_type = 'bootstrap')
}## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## pressure_self_cw 3.88 [3.68, 4.10] 1.97 0.26
## pressure_partner_cw 1.42 [1.37, 1.48] 1.19 0.70
## pushing_self_cw 1.32 [1.27, 1.38] 1.15 0.76
## pushing_partner_cw 1.16 [1.12, 1.21] 1.08 0.86
## persuasion_self_cb 1.11 [1.07, 1.15] 1.05 0.90
## persuasion_partner_cb 1.05 [1.03, 1.10] 1.03 0.95
## pressure_self_cb 1.33 [1.29, 1.39] 1.15 0.75
## pressure_partner_cb 1.17 [1.13, 1.22] 1.08 0.85
## pushing_self_cb 4.49 [4.25, 4.75] 2.12 0.22
## barriers_self_cw 4.50 [4.26, 4.76] 2.12 0.22
## barriers_self_cb 4.80 [4.54, 5.08] 2.19 0.21
## day 2.08 [1.98, 2.18] 1.44 0.48
## Tolerance 95% CI
## [0.24, 0.27]
## [0.67, 0.73]
## [0.73, 0.78]
## [0.83, 0.89]
## [0.87, 0.93]
## [0.91, 0.97]
## [0.72, 0.78]
## [0.82, 0.88]
## [0.21, 0.24]
## [0.21, 0.23]
## [0.20, 0.22]
## [0.46, 0.50]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cw 7.84 [7.40, 8.32] 2.80 0.13
## persuasion_partner_cw 9.24 [8.71, 9.81] 3.04 0.11
## pushing_partner_cb 5.73 [5.41, 6.07] 2.39 0.17
## Tolerance 95% CI
## [0.12, 0.14]
## [0.10, 0.11]
## [0.16, 0.18]
## Error in h(simpleError(msg, call)) :
## error in evaluating the argument 'x' in selecting a method for function 'print': Predictive errors are not defined for ordinal or categorical models.
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 7 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## DHARMa bootstrapped outlier test
##
## data: model.check
## outliers at both margin(s) = 1, observations = 486, p-value = 0.3
## alternative hypothesis: two.sided
## percent confidence interval:
## 0.000000000 0.002057613
## sample estimates:
## outlier frequency (expected: 0.000349794238683128 )
## 0.002057613
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(reactance_ordinal, variable = priorsense_vars)
priorsense::powerscale_plot_dens(reactance_ordinal, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(reactance_ordinal, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(reactance_ordinal, variable = priorsense_vars)
}summary_reactance_ordinal <- summarize_brms(
reactance_ordinal,
stats_to_report = stats_to_report,
rope_range = c(-0.18, 0.18),
model_rows_fixed = model_rows_fixed_ordinal,
model_rows_random = model_rows_random_ordinal,
model_rownames_fixed = model_rownames_fixed_ordinal,
model_rownames_random = model_rownames_random_ordinal,
exponentiate = T)
summary_reactance_ordinal %>%
print_df(rows_to_pack = rows_to_pack_ordinal)| OR | SE | 95% CI | pd | ROPE | inside ROPE | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|
| Intercept | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Intercept[1] | 3.82*** | 1.39 | [ 1.93, 8.02] | 1.000 | [0.84, 1.20] | 0.001 | 1.000 | 23250 | 26841 |
| Intercept[2] | 8.22*** | 3.08 | [ 4.05, 17.81] | 1.000 | [0.84, 1.20] | 0.000 | 1.000 | 23129 | 26259 |
| Intercept[3] | 22.70*** | 9.21 | [ 10.67, 52.42] | 1.000 | [0.84, 1.20] | 0.000 | 1.000 | 23388 | 25422 |
| Intercept[4] | 95.42*** | 45.43 | [ 39.79, 257.89] | 1.000 | [0.84, 1.20] | 0.000 | 1.000 | 24833 | 25331 |
| Intercept[5] | 1637.00*** | 1393.12 | [368.24, 11665.00] | 1.000 | [0.84, 1.20] | 0.000 | 1.000 | 39723 | 30374 |
| Within-Person Effects | |||||||||
| Daily individual’s experienced persuasion | 0.73** | 0.09 | [ 0.57, 0.92] | 0.995 | [0.84, 1.20] | 0.120 | 1.000 | 25197 | 26372 |
| Daily partner’s experienced persuasion | 1.01 | 0.13 | [ 0.77, 1.28] | 0.534 | [0.84, 1.20] | 0.836 | 1.000 | 34318 | 26552 |
| Daily individual’s experienced pressure | 1.76* | 0.39 | [ 1.07, 2.76] | 0.985 | [0.84, 1.20] | 0.052 | 1.000 | 22085 | 22183 |
| Daily partner’s experienced pressure | 1.23 | 0.40 | [ 0.52, 2.44] | 0.737 | [0.84, 1.20] | 0.326 | 1.000 | 18106 | 13983 |
| Daily individual’s experienced pushing | 1.31* | 0.16 | [ 1.02, 1.69] | 0.982 | [0.84, 1.20] | 0.240 | 1.000 | 33937 | 29293 |
| Daily partner’s experienced pushing | 0.90 | 0.13 | [ 0.66, 1.22] | 0.754 | [0.84, 1.20] | 0.668 | 1.000 | 31840 | 24011 |
| Day | 1.37 | 0.64 | [ 0.53, 3.48] | 0.746 | [0.84, 1.20] | 0.242 | 1.000 | 40322 | 29394 |
| Own actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Partner actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Daily barriers | 1.00 | 0.10 | [ 0.83, 1.21] | 0.504 | [0.84, 1.20] | 0.935 | 1.000 | 39235 | 30250 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | |||||||||
| Mean individual’s experienced persuasion | 1.33 | 0.93 | [ 0.33, 5.61] | 0.660 | [0.84, 1.20] | 0.187 | 1.000 | 16836 | 22501 |
| Mean partner’s experienced persuasion | 2.29 | 1.83 | [ 0.51, 12.15] | 0.854 | [0.84, 1.20] | 0.109 | 1.000 | 16531 | 21647 |
| Mean individual’s experienced pressure | 2.65 | 2.16 | [ 0.53, 13.99] | 0.884 | [0.84, 1.20] | 0.087 | 1.000 | 20515 | 28252 |
| Mean partner’s experienced pressure | 0.85 | 0.70 | [ 0.15, 4.01] | 0.582 | [0.84, 1.20] | 0.173 | 1.000 | 17717 | 24538 |
| Mean individual’s experienced pushing | 0.76 | 0.77 | [ 0.11, 6.13] | 0.608 | [0.84, 1.20] | 0.137 | 1.000 | 17231 | 22556 |
| Mean partner’s experienced pushing | 0.04* | 0.06 | [ 0.00, 0.48] | 0.994 | [0.84, 1.20] | 0.005 | 1.000 | 18520 | 24475 |
| Mean barriers | 0.93 | 0.25 | [ 0.55, 1.64] | 0.610 | [0.84, 1.20] | 0.469 | 1.000 | 20462 | 24232 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||
| sd(Intercept) | 1.08 | 0.28 | [0.61, 1.74] | NA | NA | NA | 1.000 | 11347 | 19261 |
| sd(Daily individual’s experienced persuasion) | 0.33 | 0.19 | [0.02, 0.71] | NA | NA | NA | 1.001 | 5828 | 9273 |
| sd(Daily partner’s experienced persuasion) | 0.18 | 0.14 | [0.01, 0.53] | NA | NA | NA | 1.000 | 12913 | 14882 |
| sd(Daily individual’s experienced pressure) | 0.49 | 0.32 | [0.03, 1.30] | NA | NA | NA | 1.000 | 9173 | 11304 |
| sd(Daily partner’s experienced pressure) | 0.66 | 0.53 | [0.03, 2.05] | NA | NA | NA | 1.001 | 8324 | 12613 |
| sd(Daily individual’s experienced pushing) | 0.23 | 0.18 | [0.01, 0.65] | NA | NA | NA | 1.000 | 8043 | 14635 |
| sd(Daily partner’s experienced pushing) | 0.17 | 0.15 | [0.01, 0.63] | NA | NA | NA | 1.000 | 16092 | 18227 |
| Additional Parameters | |||||||||
| sigma | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| disc | 1.00 | 0.00 | [1.00, 1.00] | NA | NA | NA | NA | NA | NA |
plot(
bayestestR::p_direction(reactance_ordinal),
priors = TRUE
) +
coord_cartesian(xlim = c(-6, 6)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
## Possible multicollinearity between b_Intercept[4] and b_Intercept[2] (r
## = 0.78), b_Intercept[4] and b_Intercept[3] (r = 0.84),
## b_pressure_partner_cb and b_persuasion_partner_cb (r = 0.74). This might
## lead to inappropriate results. See 'Details' in '?rope'.
conditional_spaghetti(
reactance_ordinal,
effects = c('persuasion_self_cw', 'pressure_self_cw')
, group_var = 'coupleID'
#, n_groups = 15
, plot_full_range = T
)\(persuasion_self_cw
<img
src="01_FinalModelsBarriers-ONLY_WITH_PLAN-_files/figure-html/report_reactance_ordinal-3.png"
width="2400" />\)pressure_self_cw
introduce_binary_reactance <- function(data) {
data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
return(data)
}
df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
for (i in seq_along(implist)) {
implist[[i]] <- introduce_binary_reactance(implist[[i]])
}
}
formula <- bf(
is_reactance ~
persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw +
persuasion_self_cb + persuasion_partner_cb +
pressure_self_cb + pressure_partner_cb +
pushing_self_cb + pushing_partner_cb +
#plan_self + plan_partner +
barriers_self_cw + barriers_self_cb +
day +
# Random effects
(persuasion_self_cw + persuasion_partner_cw +
pressure_self_cw + pressure_partner_cw +
pushing_self_cw + pushing_partner_cw | coupleID)
, decomp = 'QR'
#, autocor = autocor_str
)
prior1 <- c(
brms::set_prior("normal(0, 2.5)", class = "b")
, brms::set_prior("normal(0, 10)", class = "Intercept", lb=0, ub=5)
, brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0)
)
#brms::validate_prior(
# prior1,
# formula = formula,
# data = df_double,
# family = bernoulli()
# )
#df_minimal <- df_double[, c("AorB", all.vars(as.formula(formula)))]
is_reactance <- my_brm(
mi = use_mi,
imputed_data = implist,
formula = formula,
prior = prior1,
data = df_double,
family = brms::bernoulli(),
#control = list(adapt_delta = 0.95, max_treedepth = 15),
iter = iterations,
warmup = warmup,
chains = 4,
cores = 4,
seed = 7777,
file = file.path("models_cache_brms", paste0("is_reactance", suffix))
#, file_refit = 'always'
)## Warning: Rows containing NAs were excluded from the model.
## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance
## persuasion_self_cw 1.09 [1.06, 1.14] 1.04 0.92
## persuasion_partner_cw 1.23 [1.18, 1.28] 1.11 0.82
## pressure_self_cw 1.04 [1.02, 1.10] 1.02 0.96
## pressure_partner_cw 1.04 [1.02, 1.10] 1.02 0.96
## pushing_self_cw 1.11 [1.07, 1.15] 1.05 0.90
## pushing_partner_cw 1.23 [1.19, 1.29] 1.11 0.81
## persuasion_self_cb 2.96 [2.81, 3.13] 1.72 0.34
## persuasion_partner_cb 3.42 [3.24, 3.61] 1.85 0.29
## pressure_self_cb 2.09 [1.99, 2.20] 1.45 0.48
## pressure_partner_cb 2.06 [1.97, 2.17] 1.44 0.48
## pushing_self_cb 2.45 [2.33, 2.58] 1.56 0.41
## pushing_partner_cb 2.29 [2.18, 2.41] 1.51 0.44
## barriers_self_cw 1.06 [1.04, 1.11] 1.03 0.94
## barriers_self_cb 1.17 [1.14, 1.22] 1.08 0.85
## day 1.05 [1.02, 1.10] 1.02 0.95
## Tolerance 95% CI
## [0.88, 0.94]
## [0.78, 0.84]
## [0.91, 0.98]
## [0.91, 0.98]
## [0.87, 0.93]
## [0.78, 0.84]
## [0.32, 0.36]
## [0.28, 0.31]
## [0.46, 0.50]
## [0.46, 0.51]
## [0.39, 0.43]
## [0.41, 0.46]
## [0.90, 0.97]
## [0.82, 0.88]
## [0.91, 0.98]
## # Distribution of Model Family
##
## Predicted Distribution of Residuals
##
## Distribution Probability
## normal 34%
## beta 16%
## cauchy 16%
##
## Predicted Distribution of Response
##
## Distribution Probability
## bernoulli 75%
## beta-binomial 16%
## binomial 9%
##
## Divergences:
## 0 of 40000 iterations ended with a divergence.
##
## Tree depth:
## 0 of 40000 iterations saturated the maximum tree depth of 10.
##
## Energy:
## E-BFMI indicated no pathological behavior.
## Using 10 posterior draws for ppc type 'ecdf_overlay' by default.
## Using 10 posterior draws for ppc type 'dens_overlay' by default.
## Warning: Found 29 observations with a pareto_k > 0.7 in model 'model'. We
## recommend to set 'moment_match = TRUE' in order to perform moment matching for
## problematic observations.
##
## DHARMa outlier test based on exact binomial test with approximate
## expectations
##
## data: model.check
## outliers at both margin(s) = 1, observations = 486, p-value = 0.6217
## alternative hypothesis: true probability of success is not equal to 0.001998002
## 95 percent confidence interval:
## 0.0000520929 0.0114105115
## sample estimates:
## frequency of outliers (expected: 0.001998001998002 )
## 0.002057613
if (do_priorsense) {
gc()
priorsense::powerscale_sensitivity(is_reactance, variable = priorsense_vars)
priorsense::powerscale_plot_dens(is_reactance, variable = priorsense_vars)
priorsense::powerscale_plot_ecdf(is_reactance, variable = priorsense_vars)
priorsense::powerscale_plot_quantities(is_reactance, variable = priorsense_vars)
}summary_is_reactance <- summarize_brms(
is_reactance,
stats_to_report = stats_to_report,
model_rows_fixed = model_rows_fixed,
model_rows_random = model_rows_random,
model_rownames_fixed = model_rownames_fixed,
model_rownames_random = model_rownames_random,
exponentiate = T)
summary_is_reactance %>%
print_df(rows_to_pack = rows_to_pack)| OR | SE | 95% CI | pd | ROPE | inside ROPE | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|---|---|
| Intercept | 0.57 | 0.22 | [0.26, 1.24] | 0.923 | [0.83, 1.20] | 0.138 | 1.000 | 28387 | 31479 |
| Within-Person Effects | |||||||||
| Daily individual’s experienced persuasion | 0.69* | 0.11 | [0.48, 0.92] | 0.993 | [0.83, 1.20] | 0.093 | 1.000 | 23677 | 25761 |
| Daily partner’s experienced persuasion | 1.12 | 0.20 | [0.79, 1.66] | 0.740 | [0.83, 1.20] | 0.606 | 1.000 | 27636 | 26371 |
| Daily individual’s experienced pressure | 2.31* | 1.12 | [1.03, 9.08] | 0.978 | [0.83, 1.20] | 0.047 | 1.000 | 15749 | 15577 |
| Daily partner’s experienced pressure | 1.79 | 1.26 | [0.44, 13.34] | 0.817 | [0.83, 1.20] | 0.146 | 1.000 | 18210 | 17447 |
| Daily individual’s experienced pushing | 1.43* | 0.23 | [1.06, 2.07] | 0.990 | [0.83, 1.20] | 0.129 | 1.000 | 24749 | 27471 |
| Daily partner’s experienced pushing | 0.88 | 0.21 | [0.54, 1.50] | 0.695 | [0.83, 1.20] | 0.483 | 1.000 | 30479 | 27591 |
| Day | 1.29 | 0.69 | [0.45, 3.69] | 0.679 | [0.83, 1.20] | 0.236 | 1.000 | 42497 | 31844 |
| Own actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Partner actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Daily barriers | 1.03 | 0.12 | [0.83, 1.29] | 0.612 | [0.83, 1.20] | 0.878 | 1.000 | 39766 | 29361 |
| Daily weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Between-Person Effects | |||||||||
| Mean individual’s experienced persuasion | 3.61 | 3.66 | [0.51, 30.37] | 0.904 | [0.83, 1.20] | 0.064 | 1.000 | 18329 | 25297 |
| Mean partner’s experienced persuasion | 6.05 | 6.76 | [0.74, 67.12] | 0.953 | [0.83, 1.20] | 0.034 | 1.000 | 17821 | 25403 |
| Mean individual’s experienced pressure | 42.14* | 78.46 | [1.43, 2281.97] | 0.985 | [0.83, 1.20] | 0.008 | 1.000 | 16155 | 22563 |
| Mean partner’s experienced pressure | 0.65 | 1.39 | [0.01, 35.77] | 0.580 | [0.83, 1.20] | 0.066 | 1.000 | 12462 | 22258 |
| Mean individual’s experienced pushing | 0.48 | 0.79 | [0.02, 12.72] | 0.674 | [0.83, 1.20] | 0.079 | 1.000 | 15358 | 21859 |
| Mean partner’s experienced pushing | 0.03 | 0.06 | [0.00, 1.03] | 0.974 | [0.83, 1.20] | 0.012 | 1.000 | 15681 | 24453 |
| Mean barriers | 1.10 | 0.42 | [0.54, 2.40] | 0.598 | [0.83, 1.20] | 0.361 | 1.000 | 19221 | 25224 |
| Mean weartime | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Random Effects | |||||||||
| sd(Intercept) | 1.95 | 0.42 | [1.25, 2.94] | NA | NA | NA | 1.000 | 12148 | 22042 |
| sd(Daily individual’s experienced persuasion) | 0.47 | 0.21 | [0.06, 0.93] | NA | NA | NA | 1.001 | 7019 | 7320 |
| sd(Daily partner’s experienced persuasion) | 0.40 | 0.24 | [0.03, 0.99] | NA | NA | NA | 1.000 | 11120 | 11475 |
| sd(Daily individual’s experienced pressure) | 1.20 | 0.68 | [0.12, 2.93] | NA | NA | NA | 1.001 | 8521 | 8928 |
| sd(Daily partner’s experienced pressure) | 1.64 | 1.03 | [0.15, 4.12] | NA | NA | NA | 1.001 | 13076 | 12068 |
| sd(Daily individual’s experienced pushing) | 0.31 | 0.23 | [0.02, 0.86] | NA | NA | NA | 1.000 | 9740 | 15750 |
| sd(Daily partner’s experienced pushing) | 0.42 | 0.31 | [0.02, 1.22] | NA | NA | NA | 1.000 | 13238 | 14532 |
| Additional Parameters | |||||||||
| sigma | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot(
bayestestR::p_direction(is_reactance),
priors = TRUE
) +
coord_cartesian(xlim = c(-6, 6)) +
theme_bw()## Warning in `==.default`(dens$Parameter, parameter): longer object length is not
## a multiple of shorter object length
## Warning in is.na(e1) | is.na(e2): longer object length is not a multiple of
## shorter object length
conditional_spaghetti(
is_reactance,
effects = c('pressure_self_cw', 'pushing_self_cw'),
group_var = 'coupleID',
plot_full_range = TRUE
)\(pressure_self_cw
<img
src="01_FinalModelsBarriers-ONLY_WITH_PLAN-_files/figure-html/report_is_reactance-3.png"
width="2400" />\)pushing_self_cw
## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (exp(pressure_sel... > 0 1.52 2.67 -0.44 5.37 5.31
## Post.Prob Star
## 1 0.84
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.
summary_all_models <- report_side_by_side(
pa_sub,
pa_obj_log,
mood_gauss,
is_reactance,
stats_to_report = c('CI'),
model_rows_random = model_rows_random,
model_rows_fixed = model_rows_fixed,
model_rownames_random = model_rownames_random,
model_rownames_fixed = model_rownames_fixed
)[1] “pa_sub”
## Warning: There were 16 divergent transitions after warmup. Increasing
## adapt_delta above 0.8 may help. See
## http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
## Warning in summarize_brms(model, exponentiate = exponentiate, stats_to_report =
## stats_to_report, : Coefficients were exponentiated. Double check if this was
## intended.
[1] “pa_obj_log”
## Warning in summarize_brms(model, exponentiate = exponentiate, stats_to_report =
## stats_to_report, : Coefficients were exponentiated. Double check if this was
## intended.
[1] “mood_gauss” [1] “is_reactance”
summary_all_models <- summary_all_models %>%
print_df(rows_to_pack = rows_to_pack) %>%
add_header_above(
c(
" ", "Hurdle Component" = 2, "Non-Zero Component" = 2,
" " = 6
)
) %>%
add_header_above(
c(" ", "Subjective MVPA Hurdle Lognormal" = 4,
"Device-Based MVPA Log (Gaussian)" = 2,
"Mood Gaussian" = 2,
#"Reactance Ordinal" = 2,
"Reactance Dichotome" = 2
)
)
export_xlsx(
summary_all_models,
rows_to_pack = rows_to_pack,
file.path("Output", paste0("AllModels", suffix, ".xlsx")),
merge_option = 'both',
simplify_2nd_row = FALSE,
line_above_rows = c(1,2),
line_below_rows = c(-1)
)##
## Attaching package: 'rvest'
## The following object is masked from 'package:readr':
##
## guess_encoding
| exp(Est.)_hu pa_sub | 95% CI_hu pa_sub | exp(Est.)_nonzero pa_sub | 95% CI_nonzero pa_sub | exp(Est.) pa_obj_log | 95% CI pa_obj_log | Est. mood_gauss | 95% CI mood_gauss | OR is_reactance | 95% CI is_reactance | |
|---|---|---|---|---|---|---|---|---|---|---|
| Intercept | 2.49*** | [ 1.52, 4.04] | 49.30*** | [41.99, 57.88] | 123.18*** | [109.03, 139.10] | 3.80*** | [ 3.58, 4.02] | 0.57 | [0.26, 1.24] |
| Within-Person Effects | ||||||||||
| Daily individual’s experienced persuasion | 1.47*** | [ 1.23, 1.80] | 1.04 | [ 0.99, 1.10] | 1.02 | [ 0.99, 1.06] | 0.01 | [-0.04, 0.06] | 0.69* | [0.48, 0.92] |
| Daily partner’s experienced persuasion | 1.32** | [ 1.10, 1.63] | 1.02 | [ 0.98, 1.07] | 1.03 | [ 0.99, 1.07] | 0.01 | [-0.04, 0.06] | 1.12 | [0.79, 1.66] |
| Daily individual’s experienced pressure | 0.81 | [ 0.47, 1.48] | 0.89 | [ 0.78, 1.01] | 0.95 | [ 0.86, 1.04] | -0.05 | [-0.18, 0.08] | 2.31* | [1.03, 9.08] |
| Daily partner’s experienced pressure | 1.48 | [ 0.81, 4.99] | 0.95 | [ 0.86, 1.05] | 0.97 | [ 0.90, 1.04] | -0.06 | [-0.19, 0.06] | 1.79 | [0.44, 13.34] |
| Daily individual’s experienced pushing | 1.16 | [ 0.87, 1.57] | 0.99 | [ 0.93, 1.05] | 1.03 | [ 0.97, 1.08] | 0.04 | [-0.04, 0.11] | 1.43* | [1.06, 2.07] |
| Daily partner’s experienced pushing | 1.58** | [ 1.17, 2.44] | 0.98 | [ 0.92, 1.04] | 1.01 | [ 0.96, 1.06] | 0.11* | [ 0.02, 0.18] | 0.88 | [0.54, 1.50] |
| Day | 0.97 | [ 0.64, 1.49] | 1.01 | [ 0.88, 1.16] | 0.97 | [ 0.88, 1.07] | 0.24** | [ 0.09, 0.39] | 1.29 | [0.45, 3.69] |
| Own actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Partner actionplan | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| Daily barriers | 1.21*** | [ 1.10, 1.33] | 1.04** | [ 1.01, 1.07] | 1.03** | [ 1.01, 1.05] | 0.13*** | [ 0.10, 0.16] | 1.03 | [0.83, 1.29] |
| Daily weartime | NA | NA | NA | NA | 1.00*** | [ 1.00, 1.00] | NA | NA | NA | NA |
| Between-Person Effects | ||||||||||
| Mean individual’s experienced persuasion | 0.87 | [ 0.30, 2.52] | 1.06 | [ 0.77, 1.47] | 1.02 | [ 0.74, 1.39] | 0.14 | [-0.44, 0.72] | 3.61 | [0.51, 30.37] |
| Mean partner’s experienced persuasion | 1.26 | [ 0.44, 3.57] | 0.98 | [ 0.71, 1.34] | 0.87 | [ 0.63, 1.19] | 0.43 | [-0.15, 1.00] | 6.05 | [0.74, 67.12] |
| Mean individual’s experienced pressure | 0.30 | [ 0.06, 1.18] | 0.66 | [ 0.29, 1.45] | 1.05 | [ 0.74, 1.49] | -0.18 | [-0.78, 0.41] | 42.14* | [1.43, 2281.97] |
| Mean partner’s experienced pressure | 0.25* | [ 0.05, 1.00] | 0.52 | [ 0.23, 1.14] | 1.04 | [ 0.74, 1.47] | -0.31 | [-0.90, 0.28] | 0.65 | [0.01, 35.77] |
| Mean individual’s experienced pushing | 1.15 | [ 0.24, 5.47] | 1.54 | [ 0.86, 2.75] | 1.12 | [ 0.71, 1.78] | 0.28 | [-0.54, 1.12] | 0.48 | [0.02, 12.72] |
| Mean partner’s experienced pushing | 1.47 | [ 0.31, 7.49] | 1.57 | [ 0.88, 2.82] | 1.29 | [ 0.83, 2.05] | -0.07 | [-0.89, 0.77] | 0.03 | [0.00, 1.03] |
| Mean barriers | 1.36** | [ 1.07, 1.72] | 1.08* | [ 1.01, 1.16] | 1.05 | [ 0.99, 1.10] | 0.36*** | [ 0.28, 0.44] | 1.10 | [0.54, 2.40] |
| Mean weartime | NA | NA | NA | NA | 1.00* | [ 1.00, 1.00] | NA | NA | NA | NA |
| Random Effects | ||||||||||
| sd(Intercept) | 1.21 | [0.91, 1.65] | 0.30 | [0.22, 0.40] | 0.32 | [0.25, 0.43] | 0.61 | [0.48, 0.80] | 1.95 | [1.25, 2.94] |
| sd(Daily individual’s experienced persuasion) | 0.17 | [0.01, 0.44] | 0.11 | [0.06, 0.17] | 0.05 | [0.02, 0.09] | 0.04 | [0.00, 0.12] | 0.47 | [0.06, 0.93] |
| sd(Daily partner’s experienced persuasion) | 0.22 | [0.01, 0.50] | 0.06 | [0.01, 0.12] | 0.05 | [0.00, 0.10] | 0.04 | [0.00, 0.11] | 0.40 | [0.03, 0.99] |
| sd(Daily individual’s experienced pressure) | 0.43 | [0.02, 1.83] | 0.07 | [0.00, 0.26] | 0.06 | [0.00, 0.23] | 0.07 | [0.00, 0.27] | 1.20 | [0.12, 2.93] |
| sd(Daily partner’s experienced pressure) | 0.77 | [0.04, 2.39] | 0.06 | [0.00, 0.19] | 0.04 | [0.00, 0.13] | 0.09 | [0.00, 0.28] | 1.64 | [0.15, 4.12] |
| sd(Daily individual’s experienced pushing) | 0.43 | [0.04, 0.94] | 0.05 | [0.00, 0.14] | 0.09 | [0.02, 0.16] | 0.07 | [0.00, 0.18] | 0.31 | [0.02, 0.86] |
| sd(Daily partner’s experienced pushing) | 0.42 | [0.03, 1.15] | 0.05 | [0.00, 0.14] | 0.05 | [0.00, 0.12] | 0.10 | [0.01, 0.21] | 0.42 | [0.02, 1.22] |
| Additional Parameters | ||||||||||
| sigma | NA | NA | 0.67 | [0.65, 0.70] | 0.54 | [0.52, 0.56] | 0.88 | [0.85, 0.91] | NA | NA |
Analyses were conducted using the R Statistical language (version 4.4.2; R Core Team, 2024) on Windows 11 x64 (build 26100)